Weiming Tian , Haytham F. Isleem , Abdelrahman Kamal Hamed , Mohamed Kamel Elshaarawy
{"title":"Enhancing discharge prediction over Type-A piano key weirs: An innovative machine learning approach","authors":"Weiming Tian , Haytham F. Isleem , Abdelrahman Kamal Hamed , Mohamed Kamel Elshaarawy","doi":"10.1016/j.flowmeasinst.2024.102732","DOIUrl":null,"url":null,"abstract":"<div><div>Piano key weirs (PKWs) are an increasingly popular hydraulic structure due to their higher discharge capacity than linear weirs. Accurately predicting the discharge of PKWs is essential for appropriate design and operation. This study utilized eight Machine Learning algorithms, including non-ensemble and ensemble models to predict the discharge of type-A PKWs. Multiple-Linear-Regression (MLR), Support-Vector-Machine (SVM), Gene-Expression-Programming (GEP), and Artificial-Neural-Network (ANN) were adopted as non-ensemble models. While the ensemble models comprised Random-Forest (RF), Adaptive-Boosting (AdaBoost), Extreme-Gradient-Boosting (XGBoost), and Categorical-Boosting (CatBoost). A total of 476 experimental datasets were collected from previous research considering three critical dimensionless input parameters: PKW key widths, PKW height, and total upstream head. The models were trained on 70 % of the dataset and tested on the remaining 30 %. The hyperparameters of the models were optimized using the Bayesian Optimization technique, with 5-fold cross-validation ensuring high performance. Comprehensive analyses, including visual and quantitative methods, were employed to validate model effectiveness. CatBoost model consistently outperformed the other models, achieving the highest Determination-coefficient (R<sup>2</sup> = 0.998) and lowest Root-Mean-Squared-Error (RMSE = 0.002), highlighting its ability to handle complex data patterns and its superior optimization process. XGBoost follows closely behind, showing strong generalization, while ANN and RF perform well, but it's a slight increase in error metrics. The study also incorporated Shapley-Additive-exPlanations (SHAP) and Partial-Dependence-Plot (PDP) analyses, revealing that the total upstream head variable had the most significant impact on the discharge predictions. An interactive Graphical-User-Interface was developed to facilitate practical applications, enabling engineers to predict discharge quickly and economically.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"100 ","pages":"Article 102732"},"PeriodicalIF":2.3000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Flow Measurement and Instrumentation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0955598624002127","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Piano key weirs (PKWs) are an increasingly popular hydraulic structure due to their higher discharge capacity than linear weirs. Accurately predicting the discharge of PKWs is essential for appropriate design and operation. This study utilized eight Machine Learning algorithms, including non-ensemble and ensemble models to predict the discharge of type-A PKWs. Multiple-Linear-Regression (MLR), Support-Vector-Machine (SVM), Gene-Expression-Programming (GEP), and Artificial-Neural-Network (ANN) were adopted as non-ensemble models. While the ensemble models comprised Random-Forest (RF), Adaptive-Boosting (AdaBoost), Extreme-Gradient-Boosting (XGBoost), and Categorical-Boosting (CatBoost). A total of 476 experimental datasets were collected from previous research considering three critical dimensionless input parameters: PKW key widths, PKW height, and total upstream head. The models were trained on 70 % of the dataset and tested on the remaining 30 %. The hyperparameters of the models were optimized using the Bayesian Optimization technique, with 5-fold cross-validation ensuring high performance. Comprehensive analyses, including visual and quantitative methods, were employed to validate model effectiveness. CatBoost model consistently outperformed the other models, achieving the highest Determination-coefficient (R2 = 0.998) and lowest Root-Mean-Squared-Error (RMSE = 0.002), highlighting its ability to handle complex data patterns and its superior optimization process. XGBoost follows closely behind, showing strong generalization, while ANN and RF perform well, but it's a slight increase in error metrics. The study also incorporated Shapley-Additive-exPlanations (SHAP) and Partial-Dependence-Plot (PDP) analyses, revealing that the total upstream head variable had the most significant impact on the discharge predictions. An interactive Graphical-User-Interface was developed to facilitate practical applications, enabling engineers to predict discharge quickly and economically.
期刊介绍:
Flow Measurement and Instrumentation is dedicated to disseminating the latest research results on all aspects of flow measurement, in both closed conduits and open channels. The design of flow measurement systems involves a wide variety of multidisciplinary activities including modelling the flow sensor, the fluid flow and the sensor/fluid interactions through the use of computation techniques; the development of advanced transducer systems and their associated signal processing and the laboratory and field assessment of the overall system under ideal and disturbed conditions.
FMI is the essential forum for critical information exchange, and contributions are particularly encouraged in the following areas of interest:
Modelling: the application of mathematical and computational modelling to the interaction of fluid dynamics with flowmeters, including flowmeter behaviour, improved flowmeter design and installation problems. Application of CAD/CAE techniques to flowmeter modelling are eligible.
Design and development: the detailed design of the flowmeter head and/or signal processing aspects of novel flowmeters. Emphasis is given to papers identifying new sensor configurations, multisensor flow measurement systems, non-intrusive flow metering techniques and the application of microelectronic techniques in smart or intelligent systems.
Calibration techniques: including descriptions of new or existing calibration facilities and techniques, calibration data from different flowmeter types, and calibration intercomparison data from different laboratories.
Installation effect data: dealing with the effects of non-ideal flow conditions on flowmeters. Papers combining a theoretical understanding of flowmeter behaviour with experimental work are particularly welcome.